A sizable fraction of current research into human visuo-spatial knowledge processing explicitly or implicitly suggests a spatial processing of certain knowledge types and a visual processing of others. Similarly, many formal and technical approaches for representing and processing visuo-spatial information in artificial intelligence, in computational cognitive modeling, or in knowledge representation and reasoning explicitly or implicitly treat visual and spatial information as belonging to separate types. While there exists good evidence for some differences in mental processing of different visuo-spatial knowledge types, there is much less reason to maintain the currently ascribed separation between the visual and the spatial. We provide arguments on why strict dichotomies seem unwarranted with regard to descriptions of human mental spatial reasoning and disadvantageous for the formal and technical approaches. We build upon a synopsis of psychological evidence for the existence of multiple knowledge type specific representations in human visuo-spatial reasoning and discuss the notion of scalable representation structures. In absence of proof to the contrary, it seems better practice to assume that (a) many of the type differences attributed to visuo-spatial knowledge processing are gradual rather than qualitative in nature, and that (b) tasks involving visuo-spatial knowledge of several types are often mentally processed through dynamic associations of structures for processing basal knowledge types. The paper calls for more investigations of human reasoning in visuo-spatial tasks in which knowledge types dynamically change during reasoning. It outlines a research framework for systematically investigating different basal visuo-spatial knowledge types and their combinations with regard to cognitive and computational plausibility. Current research is related to the framework, including research on Casimir, our computational cognitive architecture for reasoning with visuo-spatial knowledge. We argue that a more systematic course of research along the lines of the proposed framework will not only lead to more appropriate descriptions of human cognition (regarding visuo-spatial knowledge processing) but may also spawn more integrated and versatile formal and technical approaches for dealing with visuo-spatial information.

Narrative is emerging as an effective medium for contextualizing learning. Narrative-centered learning environments provide engaging, story-centric virtual spaces that offer guided learning and problem-solving opportunities. Students’ ability to self-regulate learning can significantly impact performance in these environments, and, in general, is critical for academic achievement. This paper explores the relationship between narrative-centered learning and self-regulation. Connections are drawn between the salient characteristics of narrative-centered learning environments and principles for promoting and enhancing self-regulated learning in science education. These relationships are further explicated through an examination of the CRYSTAL ISLAND learning environment. The paper explores the hypothesis that narrative-centered learning environments are particularly well suited for simultaneously promoting learning, engagement, and self-regulation.

This paper describes the DynaLearn project, which seeks to address contemporary problems in science education by integrating well established, but currently independent technological developments, and utilize the added value that emerges. Specifically, diagrammatic representations are used for learners to articulate, analyse and communicate ideas, and thereby construct their conceptual knowledge. Ontology mapping is used to find and match co-learners working on similar ideas to provide individualised and mutually benefiting learning opportunities. Virtual characters are used to make the interaction engaging and motivating. The development of the workbench is tuned to fit key topics from environmental science curricula, and evaluated and further improved in the context of existing curricula using case studies. Through this approach, the DynaLearn project will deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge that fits the true nature of this expertise.

The purpose of this study was to develop and test a platform that would assist the Environmental Protection Agency (EPA), and the scientific community at large, in the generation of a human activity and energy expenditure database of sufficient detail to accurately predict human exposures and dose to various pollutants. The monitoring system developed is easily extendable to the collection of other health-related data. Our protocol tested the use of a digital voice recorder to collect activity/location diary data assuming it to be a less burdensome and a more reliable method than using paper and pencil diaries or hand-held computers. We expected the data to be more complete and reliable than retrospective reports (diaries filled out at the end of day) because the recorders are easy to use, the diary entries are made as the events occur, and we expected that participants would be more likely to complete the study because of the reduced burden. The data collection plan was also expected to show that the cost of the transcription of the diary can be reduced substantially by using speech and language processing to translate the digital diaries into the EPA’s Comprehensive Human Activity Database (CHAD).

Simulation techniques used to generate complex biological models are becoming promising research tools in oncology. Using a general Complex Adaptive Systems model that can be tailored to map various phenomena, here, we describe how this model applies to tumor growth. The multi-agent modeling environment is generated using Netlogo. The stochastic model consists of active objects including normal immune and cancer cells. The simulations conducted mimicked the tumor progression success and failure and the status of the tumor mass despite constant variations remained stable for an extended time. Furthermore, increasing the efficiency of the immune cells led to decreases in tumor cell numbers variable in both occurrence time and duration.

A novel analytical methodology has proven fruitful in developing a functional identification of consciousness with operable mental control structure in human higher brain function. Two operational homologies (one associated with language, the other tool use) derived from mammalian instrumental behavioral competence are identified, each exadaptively accessible: one a specialization of attentive search to (conventional, linguistic) internalized symbolic lexicon; the second being a combination – a co-parallel activation – of symbolically specialized attention with the original external ‘spotlight’ in order to support (deliberative, choice-making) navigational tasking. The mechanism by which consciousness becomes articulated to support the specialized control requirements of three cognitive performance levels is described, in particular for the case of the social bipedal hominid. A single articulated template model is posed to intervene between the incoherent neuronal and the coherently conscious mental level of higher brain operation. This cognitive system theory logic lends itself to an explanation of the exadaptive acquisition of a cognitively objectifiable self-model from within subjective experience, and a plausible heuristic for the systematic building of self-aware mental repertoire is discovered.

By this paper we would like to open a discussion on the need ofBy this paper we would like to open a discussion on the need of Emotional Intelligence as a feature in machines interacting with humans. However, we restrain from making a statement about the need of emotional experience in machines. We argue that providing machines computable means for processing emotions is a practical need requiring implementation of a set of abilities included in the Emotional Intelligence Framework. We introduce our methods and present the results of some of the first experiments we performed in this matter.

Thought is an essential aspect of mental function, but remains very poorly understood. In this paper, we take the view that thought is a response process — the emergent and dynamic configuration of structured response, i.e., ideas, by composing response elements, i.e., concepts, from a repertoire under the influence of afferent information, internal modulation and evaluative feedback. We hypothesize that the process of generating ideas occurs at two levels: 1) The identification of a context-specific subset — or workspace — of concepts from the larger repertoire; and 2) The configuration of plausible/useful ideas within this workspace. Workspace configuration is mediated by a dynamic selector network (DSN), which is an internal attention/working memory system. Each unit of the DSN selectively gates a subset of concepts, so that any pattern of activity in the DSN defines a workspace. The configuration of efficient and flexible workspaces is mediated by dynamical structures termed graded attractors — attractors where the set of active units can be varied in systematic order by inhibitory modulation. A graded attractor in the DSN can project a selective bias — a ``searchlight" — onto the concept repertoire to define a specific workspace, and inhibitory modulation can be used to vary the breadth of this workspace. As it experiences various contexts, the cognitive system can configure a set of graded attractors, each covering a domain of similar contexts. In this paper, we focus on a mechanism for configuring context-specific graded attractors, and evaluate its performance over a set of contexts with varying degrees of similarity. In particular, we look at whether contexts are clustered appropriately into a minimal number of workspaces based on the similarity of the responses they require. While the focus in this paper is on semantic workspaces, the model is broadly applicable to other cognitive response functions such as motor control or memory recall.

Design-based learning (DBL) has many affordances for promoting deep and lasting learning of both content and complex skills. However, careful orchestration and scaffolding are usually needed to achieve its full potential. In this paper, we describe our efforts at implementing a software suite to meet the cognitive and meta-cognitive needs of learners engaged in DBL. In Study 1, our software suite gave learners the opportunity to design in simulation, to run experiments to learn the effects of variables, and it scaffolded science explanation construction. Through our analysis of study 1 we identified both cognitive and metacognitive needs that the software did not provide for. To meet these additional requirements, we added an interactive science resource and a case library to the software to provide multi-representational content material, to facilitate exploration, and to invite metacognitive reflection needed to do well at learning through design. Learners recognized what they did not understand, took initiative to explore those science concepts, and applied them in novel ways. We present here our analysis of the kinds of metacognitive help learners need to productively learn from design activities and some ways of providing that help. Our conclusion is that cognitive aid without related metacognitive aid is insufficient in a DBL environment.

In the past years we developed several design processes (Chella et Perception, also including memory, is one of the most important al. 2006)(Cossentino and Seidita 2004)(Cossentino, Gaglio, features a robotic system must present. In (Chella and Seidita) following the approach based on Situational and Manzotti 2007) it is argued that a perception process can Method Engineering paradigm we fixed in these years be modelled and implemented as a continuous interaction (Cossentino et al. 2007)(Seidita et al. 2009). In the following loop among brain, body and environment; by continuously subsections an overview on the used SME approach, comparing actual and expected "data" coming from the environment the PASSI design process, and the robot perception loop will the robot achieves the ability to gain perceptual be given.